Self-service BI: explanation, benefits, features, do's and don'ts
Self-service business intelligence, or BI, has been on the to-do list of many organizations for quite a while.
Marketed as a tool that allows users from non-technical backgrounds to get insights at the pace of business, self-service BI, however, is leaving many organizations disappointed when it comes to implementing it practically.
Failure stories abound, with companies never getting what self-service BI has originally promised. That is freedom from IT for line-of-business users to create powerful and accurate reports to drive business growth.
In this blog, you will find out what self-service BI exactly is, why organizations fail at it, and what steps your company should take to implement a successful self-service BI solution.
What is self-service BI
Self-service BI definition
Self-service BI is often defined as a form of BI that uses simple-to-use BI tools to allow non-tech-savvy business users (sales, finance, marketing, or HR) to directly access data and explore it on their own.
Self-service BI differs from traditional BI that is owned by the IT or BI department as a centralized function. In the traditional approach, it is these teams that are in charge of everything. They prepare the required data, store and secure it, build data models, create queries, and build visualizations for end-users after collecting their requirements.
The idea of self-service BI is closely related to data democratization that is focused on letting everyone in an organization access and consume data. The ultimate purpose is to generate more insights at the organization level and drive better business decisions.
Key benefits of self-service BI
Faster time to insight
Shifting control to end-users means skipping time-consuming stages of the traditional BI process. In self-service BI, end-users don’t have to wait for days or even weeks until their report finally goes live after getting through elicitation and approvals. They also don’t have to deal with the tedious change request management process when realizing that more visuals are necessary. This is because they can chop, tweak, and add data on the fly to uncover important trends, patterns, or anomalies.
Improved operational efficiency
By empowering business users with thorough domain knowledge to perform their own data analysis on an ad-hoc basis, self-service BI produces better-quality insights while freeing the IT or BI teams from handling routine tasks related to data. Instead, these teams can focus on harder problems like setting up data pipelines to get cleansed and transformed data to the right destination at the right time and maintaining important data governance processes.
Apart from optimizing IT and BI capabilities for time and cost savings, many self-service BI adopters take a step further. They arm subject matter experts with knowledge and tools for performing advanced data analytics. In other words, they raise citizen data scientists who know how to generate ML-driven predictions critical to business. With data science talent coming at a hefty price tag, this kind of investment is probably one of the best a data-driven company can make.
Core features of self-service BI tools
To enable the powerful benefits of self-service BI mentioned above, self-service BI tools should have the following essential features:
- Data connectors that enable self-service BI tool integration with databases, CRM, ERP, marketing analytics, finance software, and other on-prem and cloud systems to serve analytics needs in the most efficient way.
- Vast reporting capabilities that range from book-quality canned reports with customizable settings to ad-hoc drill-downs while allowing users to schedule distribution or divide the results into subsets for different audiences.
- Intuitive drag-and-drop or click-based interface that allows users to select data fields and visuals and drag and drop them into report canvas for exploration and storytelling.
- Data visualization templates that simplify the process of creating dashboards based on user preferences and needs.
Many organizations take their self-service BI to the next level by enriching it with capabilities in data science and machine learning. Augmented analytics platforms enable users to discover more data, evaluate uncharacterized datasets, and create what-if scenarios. This way, business can react to its evolving needs as quickly as possible, achieving the utmost nimbleness.
Why organizations fail at self-service BI
1. Unrealistic expectations
An organization that just starts throwing data at novice users is facing a serious risk of poor-quality reports. It will be very lucky if these users with different qualifications wind up with non-misinterpreted data without first learning the basics of reporting.
For instance, a happy user creating their first report on total sales in a historic period might end up with average numbers instead of a SUM, knowing nothing about default aggregations for various measures. Or on the contrary, they can submit inflated numbers. There is also risk of data inconsistency that might affect weighted averages when they need to be displayed with different levels of granularity.
Further on, a non-power user might rest satisfied with just a casual analysis that has supported their initial beliefs. The confirmation or cherry-picking bias trap is not something an untrained user is necessarily aware of, especially when under pressure to explain a certain pattern.
2. Reporting chaos
Self-service BI doesn’t mean zero IT involvement. Letting users toy around with data with no governance from IT usually leads to reporting anarchy.
With no governance, there could be redundant reports from different users working in silos and delivering the same analysis or reports from different users analyzing the same metrics but using different filters and hence delivering conflicting results. Reports from different departments can rely on different naming conventions for quantity, value, or time or use the same terms but not necessarily the same definition. Multiple versions of the same database, errors in databases that are never fixed, the creation of objects used only once … The list is endless.
Governance is not something that a data-driven organization can boycott in the world of self-service. No matter how badly a company wants to free users for conducting their own analysis, IT still needs to be involved to maintain high data quality and consistency.
3. Lack of adoption
Truth is, not everyone likes to work hard. Most business users just want a simple dashboard that will give them the numbers. Valuable insights, however, often lie levels deeper that go beyond plain business performance analysis.
Another psychological factor that may hold back an efficient self-service BI is resistance to change. It is not uncommon for many organizations in the early stages of their self-service BI journey to see frustrated business users coming back to BI or IT to request a report as they did in the good old times. Older approaches are safer.
Unfriendly self-service BI environment setups also might be a problem. What may seem for IT or BI teams to be an easy-to-use tool for collecting and refining results can have an overwhelming and demotivating amount of features for a casual user without technical skills. Pivotal tables and spreadsheets might be dull, but users are quick to revert to them when getting stuck.
10 tips from ITRex on how to implement self-service BI successfully
Below is a list of essential takeaways from ITRex experience in building efficient self-service BI tools for both smaller business and large companies, including for the world’s leading retailer with 3 million business users:
1. Set your self-service BI strategy
You first need to define what you want to achieve with self-service BI, be it as simple as reducing delayed reports or providing data access organization-wide. Self-service can mean anything to different people, so you should be clear about your project. It’s also important to understand early the scale of implementation, the types of users, their technical proficiency, and your expectations of deliverables.
2. Keep all stakeholders on board throughout the project
You should wrap your head around what your stakeholders look for in data and their data-related success metrics. Interview them to collect their functionality, usability, user experience, and other inputs. Then continuously ask them for feedback as you iterate. Apart from making sure you build a relevant self-service BI tool, you will also give your stakeholders a sense of ownership and improve their engagement.
3. Involve the IT department
4. Set up a robust governance
Self-service BI governance encompasses the following:
- Data governance policies and procedures to ensure your data is consistent, complete, integral, accurate, and up-to-date. Here you will need to develop a broader data management strategy and adopt leading practices in master and metadata management as part of it.
- Governance of business metrics to define them uniformly across your self-service BI environment and rule out any deviations.
- Governance of reports to set a procedure for their quality validation.
- Data security to define who gets access to what data in your self-service BI and establish data lineage
5. Select the right tool
There’s no one-size-fits-all strategy. Your users have different needs and skills your tool should precisely cater for. You will probably need to balance between flexibility and sophistication to allow your users to ask new questions while staying self-reliant. A custom self-service BI solution will make it easier to achieve.
6. Establish a single source of truth
A single source of truth is implemented as part of solution architecture to enable decision-making based on the same data. For this, companies build a data warehouse or another kind of central repository that provides a 360-degree view of all their data from multiple sources and makes data access, analysis, enrichment, and protection much simpler and more efficient. It’s worth the investment.
7. Educate users
Three types of training programs for end-users are a must: 1. data analysis and visualization, 2) the basics of joining data and building data models, and 3) continuous peer-to-peer training.
8. Build a community
It will help a lot if you either establish a center of excellence or have an expert community on Slack or Teams so that your end-users know where to go to fill in gaps in knowledge.
9. Consider embedding BI specialists in business units
They will help drive engagement by increasing access to data for users with no analytical background and providing oversight as needed for better-quality reporting.
10. Start small
Choose a limited environment for starting your self-service BI project and build from there using an agile approach. This way, you will fix problems early before scaling up.
Watch this two-minute video of a project from the ITRex portfolio to learn how self-service BI augmented with AI can drive efficiency gains for a large enterprise if done right.
Author: Terry Wilson